System and Method for Generating a Patient Treatment Plan

This application is a continuation of co-pending U.S. patent application Ser. No. 18/201,716, filed May 24, 2023 by the same inventor, which is incorporated herein by reference in its entirety.

This invention relates generally to healthcare, and more particularly to patient treatment planning.

Healthcare facilities currently employ computer-based patient treatment planning systems. Such systems aid physicians in providing patients with treatment plans including drug regimens, physical regimens, specialist referrals, etc. One challenge with such systems is that they often incorrectly refer patients to specialists. As a result, many of the patients referred to specialists are turned back to their original physicians during the initial appointment without any type of treatment ever taking place. Of course, such inefficiency wastes the time and money of both the patient and the specialist involved.

The present invention overcomes the problems associated with the prior art by providing an intelligent patient referral system. Aspects of the invention facilitate the development of more efficient patient treatment plans.

An example method for providing an efficient patient treatment plan includes receiving first patient information associated with a first patient, and using the first patient information to generate a portion of a treatment plan according to predetermined criteria. The example method additionally includes receiving information indicative of the efficacy of the portion of the treatment plan, and using the information indicative of the efficacy of the portion of the treatment plan to modify the predetermined criteria to generate a second predetermined criteria. The example method additionally includes receiving second patient information associated with a second patient, and using the second patient information to generate another portion of a treatment plan according to the second predetermined criteria.

In an example method, the first patient information can be obtained from historical medical records. The information indicative of the efficacy of the portion of the treatment plan can be generated using the historical records. The historical records can include information indicative of the efficiency of a completed treatment plan of the first patient including the portion of the treatment plan.

In a particular example method, the second patient information can include a prostate volume, and the second predetermined criteria can include a prostate volume threshold. The second patient information can also include a prostate specific antigen (PSA) level, and the second predetermined criteria can include a PSA threshold value.

In another particular example method, the second patient information can include a patient survey score, and the second predetermined criteria can include at least one particular survey score value. The patient survey score can be based on answers from the second patient related to the functioning of the genitourinary system of the second patient. For example, the patient survey score can be obtained at least partially based on questions relating to the frequency of urination. As another example, the patient survey score can be obtained at least partially based on questions relating to the urgency of urination. As yet another example, the patient survey score can be obtained at least partially based on questions relating to difficulty of urination. As yet another example, the patient survey score can be obtained at least partially based on questions relating to the flow rate of urination. Optionally, the patient survey score can be obtained at least partially based on any combination of questions relating to the frequency of urination, the urgency of urination, difficulty of urination, and/or flow rate of urination.

Example systems for generating efficient patient treatment plans are also disclosed. One example system includes a user interface, a planning engine, a learning interface, and a planning engine reconfiguration routine. The user interface can be configured to receive patient information associated with a current patient and to provide information indicative of an efficient treatment plan. The planning engine can be responsive to the patient information and configured to provide a portion of a treatment plan based on the patient information. The learning interface can be configured to receive learning information indicative of a resultant efficiency of the portion of the treatment plan. The planning engine reconfiguration routine can be operative to adjust the planning engine based on the learning information.

In an example system, the learning information can be derived from historical medical records. The learning information can include prior patient information, a treatment plan followed by the prior patient, and information indicative of the efficiency of the treatment plan followed by the prior patient.

In a particular example system, the patient information associated with the current patient can include a prostate volume. The planning engine can use a prostate volume threshold to generate the portion of the treatment plan. As another example, the current patient information can include a prostate specific antigen (PSA) level, and the planning engine can use a PSA threshold value to generate the portion of the treatment plan.

In a particular example system, the patient information associated with the current patient can include a patient survey score. The planning engine can use at least one particular survey score value to generate the portion of the treatment plan. The patient survey score can be based on answers from the current patient related to the functioning of the genitourinary system of the current patient. For example, the patient survey score can be obtained at least partially based on questions relating to the frequency of urination. As another example, the patient survey score can be obtained at least partially based on questions relating to the urgency of urination. As yet another example, the patient survey score can be obtained at least partially based on questions relating to difficulty of urination. As yet another example, the patient survey score can be obtained at least partially based on questions relating to the flow rate of urination. Optionally, the patient survey score can be obtained at least partially based on any combination of questions relating to the frequency of urination, the urgency of urination, difficulty of urination and/or a flow rate of urination.

The present invention overcomes the problems associated with the prior art, by providing a system and method that generates a treatment plan based in-part on historical health records of multiple patients. In the following description, numerous specific details are set forth (e.g., type of specialists, type of treatments, etc.) in order to provide a thorough understanding of the invention. Those skilled in the art will recognize, however, that the invention may be practiced apart from these specific details. In other instances, details of well-known computing practices (e.g., machine learning, database structures, etc.) and components have been omitted, so as not to unnecessarily obscure the present invention.

1 FIG. 100 100 102 104 106 108 110 112 114 is a block diagram showing a systemfor generating efficient patient treatment plans. Systemincludes a plurality of primary care facilities, a plurality of specialist facilities, a plurality of testing/diagnostic facilities, a patient record database, a historic records database, and a health care planning system, all interconnected via an internetwork(e.g., the Internet).

102 102 Primary care facilitiesinclude, for example, general medical practitioner offices. Primary care facilitiesmay also include, but are not limited to, hospitals, mental health facilities, dental offices, and so on.

104 104 Specialist facilitiesinclude, for example, urology clinics, cardiology clinics, neurology clinics, and so on. However, specialist facilitiesmay also include various other types of specialist facilities including, but not limited to, orthopedic clinics, dermatology clinics, mental health clinics, clinics, oral surgery clinics, and any other type of specialist facility to which patients are commonly referred by primary care facilities.

106 Testing/diagnostic facilitiescan include any third party facility used for specialized medical testing and/or diagnostics. Examples of such testing/diagnostic facilities include, but are not limited to, laboratories, radiology facilities, ultrasound facilities, and so on.

108 108 102 112 102 Patient records databaseincludes medical records of patients associated with one or more primary care facilities. Patient records databasemay include one or more remote databases accessible to primary care facilitiesindirectly through networkand/or may include one or more local databases directly accessible within primary care facilities.

110 110 102 104 106 108 Historic records databaseincludes information associated with past patient courses of treatment and the resulting outcomes. For example, historic records databasemay include a patient treatment plan that instructed a patient to take a particular medication and how effective the medication was, specialist referrals, diagnostic tests, and so on. Such records may be acquired from primary care facilities, specialist facilities, testing/diagnostic facilities, patient database, and/or any other source of records relating to patient treatments and outcomes.

112 108 110 112 112 112 102 104 106 Health care planning systemis configured to generate a patient treatment plan based at least in part on information acquired directly from the patient (e.g., physical exam, patient questionnaire, etc.), from patient records database, and/or historic records database. Although health care planning systemis shown as a remote system accessible through network, health care planning systemmay also be located partially or completely in any one or more of primary care facilities, specialty facilities, and/or testing/diagnostic facilities.

2 FIG. 200 112 200 112 202 204 206 208 210 212 200 is a block diagram of a serverof health care planning system, which is configured to generate patient treatment plans. Serveris connected to networkand includes one or more processing unit(s), a network interface, non-volatile memory, a local user interface, and a working memory, all interconnected by a system bus, which facilitates intercommunication between the various components of server.

202 210 200 210 Processing unit(s)execute data and code contained in working memoryto cause serverto carry out its intended functions (e.g. generate patient treatment plans). For illustrative purposes, subsets of code are represented in working memoryas functional blocks. However, this is by way of example and explanation only. The present invention is not limited by any particular arrangement or structure of the computer code, unless explicitly set forth in the appended claims.

200 112 204 204 112 200 102 104 106 108 110 Servercommunicates over networkvia network interface. Network interfacetransmits data packets onto and receives data packets from internetwork, thus allowing serverto communicate with primary care facilities, specialist facilities, testing/diagnostic facilities, patient records database, and historic records database.

206 200 Non-volatile memory(e.g., read-only memory, hard drive(s), etc.) provides storage for data and code (e.g., boot code and programs) that are retained even when serveris powered down.

208 200 Local user interfacefacilitates communications local users and includes, by way of nonlimiting example, a keyboard, a mouse, a monitor, a printer, and other such devices that facilitate communications between serverand a user and/or administrator.

210 200 214 210 214 210 Working memory(e.g. random access memory) provides dynamic memory to server, and can store executable code (e.g. an operating system), which is loaded into working memoryduring system start-up. Operating systemfacilitates control and coordination of other modules loaded into working memory.

210 216 218 220 222 224 226 210 206 216 200 102 104 106 108 110 Working memoryfurther includes a provider communication module, current patient information, a health care planning module, planning criteria, historical patient information, and a planning criteria modifier module. The various modules and data are initialized and loaded into working memoryat startup from non-volatile memoryusing methods well known to those skilled in the art. Communication modulefacilitates communication between serverand any one or more of primary care facilities, specialist facilities, testing/diagnostic facilities, patient records database, and historic records database.

218 220 218 222 222 218 220 Current patient informationincludes information associated with a patient that is currently seeking a treatment plan. Such information may include, without limitation, results of a physical examination, diagnostic results, answers to a questionnaire, and/or general information about the patient such as, for example, age, sex, weight, height, blood pressure, etc. Health care planning modulegenerates treatment plan(s) for the patient based at least in-part on current patient informationand planning criteria. Planning criteriaincludes predetermined criteria for generating the patient treatment plan based on patient information. For example, one predetermined criteria may be a specific prostate volume that when exceeded causes health care planning moduleto generate a patient treatment plan that includes referring the patient to a specialist (e.g. a Urologist).

224 110 Historical patient informationincludes historical patient information from, for example, historic records database. This historical patient information may include past treatment plans and outcomes resulting therefrom associated with a number of other past patients. For example, such historical patient information may include a treatment plan that referred a patient to a urologist (e.g., because his prostate size exceeded a specific volume), but where the ultimate successful therapy could have been provided by the general practitioner, thereby avoiding an unnecessary visit to the urologist. In other words, a referral to a specialist was not justified by the planning criteria. In general, historical patient information can include a great multitude of examples of efficient and inefficient treatment plans for past patients associated with particular patient information (e.g., the past patient's history, exam results, diagnostic results, and so on.

226 222 224 220 226 Planning criteria modifieris configured to modify the planning criteria in planning criteria module, using historical patient informationas a guide, to improve the efficacy of health care plans generated by health care planning module. For example, planning criteria modifier modulemay increase a prostate volume criterion, if the current volume criterion results in more unnecessary visits to a urologist than the updated criterion. As a result, subsequently generated patient treatment plans will be less likely to result in an unnecessary referral.

3 FIG. 300 302 300 304 306 308 306 310 306 312 306 314 306 316 306 318 1 2 3 4 5 6 1 2 3 4 5 6 N is a flowchart summarizing an example method/questionnaireused to obtain a score from a patient that can be used to generate a patient treatment plan. In a first step, it is determined if the patient is a biological male over the age of fifty. If not, methodends. If so, then in a second step, the number of times within a month that the patient has had to urinate less than two hours after the previous urination is determined, a corresponding value is assigned according to a key, and the value is recorded as N. Then, in third step, the number of times within a month that the patient has stopped and restarted urination is determined, a corresponding value is assigned according to key, and the value is recorded as N. Next, in a fourth step, the number of times within a month that the patient has had a sensation of not completely emptying their bladder after urination is determined, a corresponding value is assigned according to keyand the value recorded as N. Then, in a fifth step, the number of times within a month that the patient has found it difficult to postpone urination is determined, a corresponding value is assigned according to keyand, and the value is recorded as N. Next, in a sixth step, the number of times within a month that the patient has had a weak urine stream is determined, a corresponding value is assigned according to key, and the value is recorded as N. Then, in a seventh step, the number of times within a month that the patient has had to push or strain to urinate is determined, a corresponding value is assigned according to key, and the value is recorded as N. Finally, in an eighth step, the summation of N+N+N+N+N+Nis determined and recorded as Σ.

4 FIG.A 400 300 402 220 400 404 406 408 410 is a flowchart summarizing an example methodA for generating a first portion of a patient treatment plan using the input from method/questionnaire. In a first step, health care planning moduledetermines if 3≤ΣN≤8. If not,A ends, and no recommended plan is generated. If 3≤ΣN≤8, then in a second step, the portion of the patient treatment plan indicates that the patient needs prostate specific antigen (PSA) testing and treatment with medication for benign prostatic hypertrophy (BPH). Next, in a third step, it is determined if the medication for the BPH is effective. If so, in a fourth step, the patient treatment plan indicates that the patient should continue the BPH medication. If the BPH medication is not effective, then in a fifth step, the patient treatment plan instructs the patient to continue for ultrasound and urine testing to confirm the need for prostatic artery embolization (PAE).

4 FIG.B 400 300 412 400 412 414 416 400 418 420 420 400 418 420 400 422 400 418 422 424 400 418 426 N N N is a flowchart summarizing an example methodB for generating a second portion of a patient treatment plan using the input (e.g., prostate score Σ) from method/questionnaire. In a first step, it is determined if Σ>7. If not, methodB ends, and no additional treatment plan is recommended. If, in first step, it is determined that Σ>7, then in a second step, the second portion of the patient treatment plan will indicate that the patient needs further testing and treatment, and information related to past medical history, any history of PSA levels, and any past medication prescribed for BPH should be obtained. Next, in a third steep, it is determined if the patient's PSA level is greater than 4. If so, methodB proceeds to a fourth step, and the second portion of the patient treatment plan will include a referral to a urologist. If the PSA level is not greater than 4, then, in a fifth step, it is determined if the patient has a history of prostate surgery. If, in step, it is determined that the patient does have a history of prostate surgery, methodB proceeds to fourth step, and the second portion of the patient treatment plan will include a referral to a urologist. If, in step, it is determined that the patient does not have a history of prostate surgery, then methodB proceeds to a sixth step, where it is determined if the patient has a history of prostate cancer. If it is determined that the patient does have a history of prostate cancer, methodB proceeds to fourth step, and the second portion of the patient treatment plan will include a referral to a urologist. If, in sixth step, it is determined that the patient does not have a history of prostate cancer, then in a seventh stepit is determined if the patient has a history of urethral stricture or surgery. If the patient does have a history of urethral stricture or surgery, methodB proceeds to fourth step, and the second portion of the patient treatment plan will include a referral to a urologist. If it is determined that the patient does not have a history of urethral stricture or surgery, then, in an eighth step, the second portion of the patient treatment plan will include a recommendation for ultrasound and urine testing to confirm the need for PAE.

5 FIG. 500 300 502 504 506 502 508 510 500 506 510 512 514 512 516 N N is a flowchart summarizing a methodfor generating a patient treatment plan following a period of use of BPH medication, using the input from questionnaire. In a first step, it is determined if the prostate score is greater than 3: Σ>3. If not, then in a second step, it is concluded that the medication is effective and, in a third step, the treatment plan includes instructing the patient to continue medication and follow up at some future time (e.g., in six months). If, in first step, it is determined that Σ>3, then in a fourth step, it is concluded that the medication is not effective. Next, in a fifth step, it is determined if the patient wants additional treatment. If not, then methodproceeds to third step, and the treatment plan will include instructing the patient to continue medication and follow up at a later date (e.g., in six months). If, however, it is determined in stepthat the patient wants treatment, then, in a sixth step, it is determined if the volume of the patient's prostate is less than 30 ml. If so, then in a seventh step, the patient treatment plan will include a referral to a urologist. If it is determined in sixth stepthat the volume of the patient's prostate is 30 ml or more, then in an eighth step, the patient treatment plan will indicate that the patient is a good candidate for PAE, the patient is offered PAE treatment, and the patient can be informed that the PAE treatment has less side effects, a faster recovery time, is non-surgical, and is an in-office procedure.

6 FIG. 600 112 112 602 604 606 604 606 606 604 604 604 604 604 is a block diagramillustrating machine learning and pre-training of health care planning system. In training system, data from a known data setis provided to a modifiable predictive process (MPP)and a comparison process. The data set includes, for example, historical patient information (e.g., prostate size, age, lab results, and so on), provided treatments, and actual outcomes corresponding to that patient information. Modifiable predictive processgenerates a treatment plan based on the provided patient information, and the generated treatment plan is output to comparison process. Comparison processthen compares the generated patient treatment plan with a known efficient and effective patient treatment plan and then outputs difference indicators and values back to modifiable predictive process. Modifiable predictive processthen adjusts weighted values within treatment plan generating algorithms to minimize the difference between the generated plan and the known effective and efficient treatment plan. This training process is repeated with extremely large data sets, until the patient treatment plans output from modifiable predictive processaccurately reflects the known efficient and effective patient treatment plans. This feedback loop may continue for as many iterations as needed until modifiable predictive processcan output accurate patient treatment plans. In other words, modifiable predictive processis machine learning system that is pre-trained with known data before it is used to generate novel patient treatment plans for patients based on current patient information.

112 112 112 604 7 FIG. In addition to pre-training, planning systemcan also learn in real-time from patient information associated with current patients following generated patient treatment plans.is a block diagram illustrating ongoing, real-time machine learning and training of health care planning system. In system, modifiable predictive processreceives input, generates a portion of a patient treatment plan based on the input, receives feedback based on the implementation of the plan, and adjusts its predictive process ‘(“learns”) to generate a more accurate patient treatment plan from the feedback.

604 702 702 MPPreceives input in the form of current patient information. Current patient informationincludes medical information associated with the current patient being treated, including for example, but not limited to, past medical history, family medical history, results of physical exams, diagnostic information, and so on.

604 1 N 1 N 1 N 1 N 1 N 1 N Based on the current patient information, MPPgenerates an initial portion of a treatment plan, which can include, but is not limited to, one or more diagnostic/testing requests (D-D), specialist referrals (S-S), and/or treatment plans (T-T). Diagnostic/testing requests (D-D) can include, by way of non-limiting example, blood tests, urine tests, imaging studies, biopsies, and so on. Specialist referrals (S-S) are patient referrals to medical specialists, which can include, by way of non-limiting example, referrals to urologists, cardiologists, dermatologists, or any other medical specialist. Treatment plans (T-T) can include, by way of non-limiting example, drug therapy, physical therapy, dietary restrictions, surgical intervention, and so on.

1 N 1 N 1 N 604 710 712 714 714 604 Any results of diagnostic/testing requests (D-D), specialist referrals (S-S), and treatment plans (T-T) are then fed back into MPPthrough a learning input. The results are also provided back into the primary care facility, via a communication path, to update the current patient information to generate updated patient information. The updated patient informationcan then be provided back into RPPto generate a next portion of the treatment plan for the current patient. This process is continually repeated as many times as are necessary to resolve the current patient's medical issues.

710 604 604 This feedback received via learning inputis accumulated by MPPand can be used to determine the effectiveness of each recommended portion of the patient treatment plan. Based on the determined efficiency and effectiveness of each portion of the generated patient treatment plans, the predictive processes of MPPcan be modified to generate ever more efficient and effective patient treatment plans as time goes on.

8 FIG. 800 802 804 806 808 810 808 812 814 816 818 820 822 824 800 804 800 824 804 826 is a flowchart summarizing a methodfor generating an efficient patient treatment plan. In a first step, patient information is received. Then, in a second step, the received patient information is compared to predetermined criteria. Next, in a third step, it is decided whether any treatment is recommended. If so, then in a fourth step, the recommended treatment is provided. Then, in a fifth step, the patient information is updated to reflect the treatment provided in fourth step. Next, in a sixth step, it is decided if any diagnostic/testing is recommended. If so, the recommended diagnostic/testing is carried out in a seventh step. Then, in an eighth step, the patient information is updated to include information indicative of the results of the diagnostic/testing. Next, in a ninth step, it is determined if a referral to a specialist is recommended. If so, then in a tenth step, a specialist referral is completed. Then, in an eleventh step, the patient information is updated to include information indicative of the report of the specialist. Next, in a twelfth step, it is determined whether the patient's issue is resolved. If not, methodreturns to second step, and methodis repeated, using the updated patient information to generate a next segment of the current patient treatment plan. If, however, it is determined in twelfth stepthat the patient's issue is resolved, the predetermined criteria of stepcan be updated in a step, based on any perceived inefficiencies and/or ineffectiveness of portions of in the overall patient treatment plan.

The description of particular embodiments of the present invention is now complete. Many of the described features may be substituted, altered or omitted without departing from the scope of the invention. For example, alternate questionnaires will be used for different specialist types (e.g., orthopedic surgeon, cardiologist, etc.), instead of the urology-based questionnaire. As another example, the invention may be implemented in other health care facilities outside of medical facilities such as dentist offices, chiropractic offices, etc. These and other deviations from the particular embodiments shown will be apparent to those skilled in the art, particularly in view of the foregoing disclosure.